31/08/2026
🔬 How can help scientists see what cannot be directly observed?
Many scientific challenges share the same goal: reconstructing images, signals, or physical states from incomplete, noisy, or indirect measurements. Whether in medical imaging, physics, astronomy, or climate science, researchers often need to make sense of limited data and infer what lies beyond what can be directly measured.
🔍 On September 3, during 14/2026 , Professor of Statistical Machine Learning at the University of Oxford and Research Director at Google DeepMind, will explore how recent advances in generative AI are transforming the way scientists tackle these inverse problems.
❔ Rather than producing a single answer, they can identify multiple plausible solutions and quantify the confidence associated with them, a critical capability in scientific discovery and decision-making.
📍 OGR Torino – Mezzanino
📅 September 3, 2026 | 04:00–06:00 PM CEST
👉 To learn more and register: https://ai4i.it/ias-14-2026-yee-whye-teh/